Coordinated control method for energy storage power station system based on Carnot cycle battery

Abstract

The present application relates to the field of energy storage power station system control technology, and specifically to a coordinated control method for an energy storage power station system based on a Carnot cycle battery. The method includes: determining the changing characteristic values ​​of various parameters of each wind chamber during each Carnot cycle, and clustering all historical Carnot cycle processes; judging whether the historical Carnot cycle process can be used for parameter control in subsequent Carnot cycle processes based on the clustering results; and determining the effectiveness of each cluster, performing a weighted average of the average values ​​of various parameters in all Carnot cycle processes within the corresponding cluster, obtaining the optimal values ​​of various parameters, and controlling various parameters in subsequent Carnot cycle processes through a controller. The present application aims to reduce the instability of wind chamber parameters during the subsequent operation of a solid heat storage system and improve the overall heat utilization efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of energy storage power station system control, and specifically to a coordinated control method for an energy storage power station system based on a Carnot cycle battery. Background Art

[0002] An energy storage power station is a facility that converts renewable energy into electrical energy and stores it so that it can supply electricity during peak demand periods or when the power system fails. With the rapid development of renewable energy, energy storage technology has become the key to balancing the supply and demand of the power grid and improving energy utilization efficiency. Carnot cycle battery energy storage technology is mainly based on the storage and conversion of thermal energy. Through heat pump cycles and heat engine cycles, it stores and releases electrical energy by absorbing and releasing heat at different temperatures, thereby realizing the conversion between electrical energy and thermal energy. The coordinated control of the energy storage power station system aims to improve the stability and reliability of the power grid by optimizing the coordinated operation of various components in the energy storage power station system. Among them, thermal storage (solid thermal storage) energy storage power stations are a relatively common technology. At present, the Carnot cycle principle is widely used in solid thermal storage systems.

[0003] The solid heat storage device generates high-temperature hot air from the high-temperature air chamber and enters the waste heat boiler. After heat exchange in the shell and tube system, the low-temperature air enters the low-temperature air chamber of the solid heat accumulator through the low-temperature air duct. After being heated by the solid heat accumulator, it enters the high-temperature air chamber again and is continuously fed into the shell and tube system of the heat boiler, completing a closed cycle over and over again. During this entire cycle, due to irreversible losses such as the thermal resistance of the heat exchanger, the parameters of the air chamber will fluctuate unstably during operation, resulting in insufficient heat exchange and low overall heat utilization efficiency. Summary of the Invention

[0004] In order to solve the above technical problems, the present application provides a coordinated control method of an energy storage power station system based on Carnot cycle batteries to solve the existing problems.

[0005] The collaborative control method of the energy storage power station system based on the Carnot cycle battery of the present application adopts the following technical solutions:

[0006] An embodiment of the present application provides a coordinated control method for an energy storage power station system based on a Carnot cycle battery, the method comprising the following steps:

[0007] S1, obtaining various parameters collected by each wind chamber at all collection times during several historical Carnot cycles of the energy storage power station coefficient;

[0008] S2, based on the range and overall change trend of the change curve of each parameter of each air chamber during the Carnot cycle, determine the change characteristic value of each parameter of each air chamber during each Carnot cycle;

[0009] S3, based on the average level of the difference in the characteristic values ​​of various parameters between Carnot cycle processes under the same wind chamber, all historical Carnot cycle processes are clustered;

[0010] S4, based on the number of clusters in the clustering results, the maximum number of elements in the clusters and the difference between the number of all historical Carnot cycle processes, determine the stability and judge whether the historical Carnot cycle process can be used for parameter control in the subsequent Carnot cycle process;

[0011] S5, determining the effectiveness of each cluster based on the number of elements in each cluster and the degree of disorder of the order of distribution of all elements in the Carnot cycle process according to the order of collection; normalizing the effectiveness and recording it as a weight value;

[0012] S6, using the weight value to perform weighted averaging on the average values ​​of various parameters in all Carnot cycle processes in the corresponding cluster, to obtain the optimal value of each parameter, and to control various parameters in subsequent Carnot cycle processes through the controller.

[0013] Preferably, the types of the air chambers include high-temperature air chambers and low-temperature air chambers.

[0014] Preferably, the variation curve is obtained by curve fitting the data collected at all collection moments for each parameter of each wind chamber during the Carnot cycle.

[0015] Preferably, the change characteristic value is determined by the product of the range and the overall change trend.

[0016] Preferably, the overall change trend is further determined by the average slope of all points corresponding to the acquisition moments on the change curve.

[0017] Preferably, when clustering all historical Carnot cycle processes, the clustering distance between the Carnot cycle processes is further determined as:

[0018] Under the same parameter of the same wind chamber, the difference between the change characteristic values ​​in any two historical Carnot cycle processes is calculated; the average level of the difference of all parameters under all wind chambers is used as the clustering distance between any two historical Carnot cycle processes.

[0019] Preferably, in step S4, the stability is further determined by a ratio of the difference to the number of clusters.

[0020] Preferably, when the stability is greater than or equal to a preset stability threshold, the historical Carnot cycle process can be used for parameter control in subsequent Carnot cycle processes.

[0021] Preferably, the effectiveness is determined by the ratio of the number of elements in the corresponding cluster to the degree of disorder of the order distribution.

[0022] Preferably, the method for controlling various parameters in the subsequent Carnot cycle process by the controller is:

[0023] The optimal value is used as the ideal value of the corresponding parameter, and the deviation between the ideal value and the measured value is used as the input of the controller, and the controller controls the corresponding parameter in the subsequent Carnot cycle process.

[0024] This application has at least the following beneficial effects:

[0025] In this application, first, by analyzing the fluctuations of various parameters in the high-temperature wind chamber and the low-temperature wind chamber during each historical Carnot cycle process, the change characteristics of various parameters are characterized; secondly, according to the average difference of the change characteristic values ​​of each parameter under the same wind chamber as the clustering distance, all Carnot cycle processes are clustered, so as to cluster the Carnot cycle processes with smaller parameter change characteristic value distances into one category, which is convenient for subsequent overall analysis of Carnot cycle processes of the same category and reduces the problem of inaccurate prediction results generated by the analysis of a single Carnot cycle process; then, by analyzing the clustering results, the stability of the solid heat storage system during the historical Carnot cycle process is quantified, and the accuracy of parameter control judgment in the subsequent Carnot cycle process is improved; thirdly, by analyzing the continuity between the Carnot cycle processes in each cluster, the effectiveness of the Carnot cycle process in the cluster is characterized, and the reference degree of the cluster for the wind chamber parameters in the subsequent Carnot cycle process is calculated; finally, by performing a weighted analysis on the wind chamber parameters in the historical Carnot cycle process, the optimal parameters of the wind chamber in the subsequent Carnot cycle process are obtained, thereby reducing the instability of the wind chamber parameters during the subsequent operation of the solid heat storage system and improving the overall heat utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present application or the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0027] Figure 1 A flow chart of a coordinated control method for an energy storage power station system based on a Carnot cycle battery provided in this application;

[0028] Figure 2 A schematic diagram of a solid heat storage system based on a Carnot cycle provided for one embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to further illustrate the technical means and effects adopted by this application to achieve the predetermined invention objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features and effects of the coordinated control method of the energy storage power station system based on the Carnot cycle battery proposed in this application. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics of one or more embodiments may be combined in any suitable form.

[0030] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0031] The specific scheme of the coordinated control method of the energy storage power station system based on the Carnot cycle battery provided in this application is described in detail below with reference to the accompanying drawings.

[0032] An embodiment of the present application provides a coordinated control method for an energy storage power station system based on a Carnot cycle battery.

[0033] Specifically, the following coordinated control method for energy storage power station system based on Carnot cycle battery is provided. Figure 1 , the method comprises the following steps:

[0034] S1, obtaining various parameters collected from each wind chamber at all collection times during several historical Carnot cycles of the energy storage power station system.

[0035] This embodiment takes a heat storage type (solid heat storage) energy storage power station as an example for analysis. In this embodiment, the schematic diagram of the solid heat storage system based on the Carnot cycle is as follows: Figure 2 As shown, in Figure 2 In the present invention, the solid heat storage system is mainly composed of 1. solid heat storage equipment, 2. waste heat boiler, 3. return air duct, 4. outlet air duct and 5. fan.

[0036] During the entire operation of the solid heat storage system, it is very important to monitor key parameters such as the temperature, flow rate, and pressure data of the hot or cold air generated by the high-temperature air chamber and the low-temperature air chamber.

[0037] Based on this, the present application first obtains various parameters in the high-temperature wind chamber and the low-temperature wind chamber of each historical Carnot cycle process at all collection moments, where the data collection frequency is 1 second and the collection period is a complete Carnot cycle process.

[0038] At this point, the above method can be used to obtain various parameters collected by each wind chamber at all collection times during several historical Carnot cycles of the energy storage power station system. The number of historical collection times during the Carnot cycle process is set by the implementer.

[0039] S2, based on the range and overall change trend of the change curve of each parameter of each air chamber during the Carnot cycle, determine the change characteristic value of each parameter of each air chamber during each Carnot cycle.

[0040] The parameter data of the high-temperature and low-temperature air chambers during the M historical Carnot cycles were obtained. First, the fluctuation of the parameters in the high-temperature and low-temperature air chambers during each historical Carnot cycle was analyzed to characterize the variation characteristics of the parameters.

[0041] Preferably, in this embodiment, the change characteristic value is determined by the product of the range and the overall change trend.

[0042] Specifically, as an implementation method, taking the temperature parameter in the mth historical Carnot cycle as an example, the mathematical formula of the corresponding temperature parameter change characteristic value is:

[0043] θ m,a =(Te max -Te min )×l m,a

[0044] Where θ m,a Indicates the characteristic value of the temperature parameter change of the a-th air chamber during the m-th Carnot cycle; when a=1, it indicates a high-temperature air chamber, and when a=2, it indicates a low-temperature air chamber; Te max It represents the maximum temperature in the variation curve of the temperature parameter of the a-th air chamber during the m-th Carnot cycle, Te min Indicates the minimum temperature value in the temperature parameter variation curve of the a-th air chamber during the m-th Carnot cycle; l m,a It represents the slope of the change curve of the a-th air chamber temperature parameter during the m-th Carnot cycle. This value is represented by the mean slope of the corresponding points at all sampling moments on the change curve.

[0045] The variation curve is obtained by curve fitting the data collected at all collection moments for each parameter of each air chamber during the Carnot cycle. This embodiment uses the least squares method for curve fitting, which is a well-known technique and will not be described in detail.

[0046] It should be understood that |Te max -Te min The smaller the value of |, the smaller the temperature fluctuation range of the a-th air chamber during the m-th Carnot cycle. m,a The smaller the value of , the more stable the corresponding temperature fluctuation. m,a The smaller the value of , the smaller the characteristic value of the temperature parameter change of the a-th air chamber during the m-th Carnot cycle.

[0047] As another embodiment, the overall change trend is l m,a It can also be determined by the slope of the straight line obtained by performing a straight-line fitting on the variation curve of the temperature parameter of the a-th wind chamber during the m-th Carnot cycle.

[0048] Similarly, the changing characteristic value of each parameter of each wind chamber during the historical M Carnot cycles can be calculated.

[0049] S3, based on the average level of the difference in the characteristic values ​​of various parameters between Carnot cycle processes under the same wind chamber, all historical Carnot cycle processes are clustered.

[0050] The method of step S2 can be used to calculate the characteristic value of each parameter change for each air chamber during the M historical Carnot cycles. Then, for all historical Carnot cycles, the average difference in the characteristic value change of each parameter for the same air chamber is used as the cluster distance to cluster all Carnot cycles. This is used to group Carnot cycle processes with smaller parameter characteristic value distances into one category. This facilitates subsequent holistic analysis of Carnot cycle processes of the same category and reduces the problem of inaccurate prediction results caused by analyzing a single Carnot cycle process.

[0051] The specific clustering process is as follows: first, the differences in the changing characteristic values ​​of various parameters under the same wind chamber during different Carnot cycles are calculated to obtain the clustering distances between the Carnot cycle processes.

[0052] Preferably, in this embodiment, under the same parameter of the same wind chamber, the difference between the change characteristic values ​​in any two historical Carnot cycle processes is calculated; and the average level of the difference of all parameters under all wind chambers is used as the clustering distance between any two historical Carnot cycle processes.

[0053] As an implementation method, the clustering distance ρ between the p-th and q-th Carnot cycle processes is used. p,q For example, the expression is as follows: Where, ρ p,q represents the clustering distance between the pth and qth Carnot cycle processes, K represents the number of parameters in the Carnot cycle process, θ p,a,k ,θ q,a,k They represent the changing characteristic values ​​of the kth parameter of the ath wind chamber during the pth and qth Carnot cycles respectively.

[0054] Then, all historical Carnot cycle processes are clustered using the K-means clustering algorithm, where the cluster distance between different Carnot cycle processes is calculated using the ρ p,qTo quantify, the number of clusters is determined using the silhouette coefficient method. Among them, the silhouette coefficient method and the K-means clustering algorithm are both well-known technologies and will not be described in detail. In other embodiments of the present application, other suitable clustering algorithms can also be selected, for example: density clustering algorithm.

[0055] Assume that after clustering is completed, a total of N clusters are obtained, wherein the change characteristic values ​​of the wind chamber parameters during the Carnot cycle in each cluster are relatively similar, that is, the difference between the change characteristic values ​​is small.

[0056] S4, based on the number of clusters in the clustering results, the maximum number of elements in the clusters and the difference between the number of all historical Carnot cycle processes, determine the stability and judge whether the historical Carnot cycle process can be used for parameter control in the subsequent Carnot cycle process.

[0057] In a Carnot cycle-based solid heat storage system, the fluctuations in wind chamber parameters between different Carnot cycles should be relatively small. However, large fluctuations in parameters can lead to system instability, affecting the continuity and reliability of the heat storage and release processes. Using parameter data obtained from unreliable past Carnot cycles to control parameters in subsequent Carnot cycles can affect the judgment of parameter control in subsequent Carnot cycles.

[0058] Therefore, this application first determines the stability of the solid heat storage system in the historical Carnot cycle process by calculating the stability of the wind chamber parameter fluctuations between the Carnot cycle processes in the clustering results, so as to judge whether the historical Carnot cycle process can be used for parameter control in the subsequent Carnot cycle process.

[0059] It should be noted that the fewer the number of clusters in the clustering results, the more concentrated the fluctuations of the wind chamber parameters in the historical Carnot cycle process. At the same time, the more concentrated the Carnot cycle process is in a certain cluster, the smaller the fluctuation difference of the wind chamber parameters in the corresponding historical Carnot cycle process is, and the more stable the operation of the corresponding solid heat storage system is.

[0060] Preferably, in this embodiment, the method for determining the stability is specifically: calculating the difference between the maximum number of elements in the cluster and the number of all historical Carnot cycle processes; and taking the ratio of the difference to the number of clusters as the stability.

[0061] As an implementation method, the mathematical formula for the stability of the solid heat storage system during the specific historical Carnot cycle process is as follows: Where α represents the stability of the solid thermal storage system during the historical Carnot cycle process, N represents the number of clusters after clustering all historical Carnot cycle processes, and M represents the number of historical Carnot cycle processes. Indicates the maximum number of Carnot cycle processes contained in N clusters.

[0062] It should be understood that The larger the value of , the fewer the types obtained after clustering, and the more concentrated the parameter changes in the Carnot cycle process; The larger the value of , the greater the proportion of the Carnot cycle processes in the cluster with the largest number of Carnot cycle processes, and the more concentrated the corresponding Carnot cycle processes are in a certain cluster. Therefore, the larger the value of α, the better the stability of the solid heat storage system in the historical Carnot cycle process.

[0063] In other embodiments of the present application, the mathematical formula for stability can also be expressed as Sure.

[0064] Furthermore, if the stability is less than the stability threshold, it means that the stability of the solid heat storage system in the historical Carnot cycle process is relatively poor, which may be caused by material problems, design defects, etc., resulting in poor heat exchange efficiency of the entire system, and maintenance personnel need to be arranged to inspect the entire heat storage system.

[0065] If the stability is greater than or equal to the stability threshold, the wind chamber parameters in the subsequent Carnot cycle process are found out by analyzing the changes in the wind chamber parameters in the historical Carnot cycle process for coordinated control.

[0066] The stability threshold is set to 0.4 in this embodiment and can be set by the implementer.

[0067] S5, based on the number of elements in each cluster and the degree of disorder of the order distribution of all elements corresponding to the Carnot cycle process according to the collection order, determine the effectiveness of each cluster; normalize the effectiveness and record it as a weight value.

[0068] Specifically, for each Carnot cycle process in a cluster, the higher the continuity between different Carnot cycles, the smaller the difference in the wind chamber parameters between adjacent Carnot cycles during the operation of the solid thermal energy storage system. This indicates that the effectiveness of the Carnot cycle process in the corresponding cluster is higher, and its reference degree for the wind chamber parameters in subsequent Carnot cycles is also greater. Therefore, the effectiveness of the Carnot cycle process in each cluster can be calculated.

[0069] Preferably, in this embodiment, the ratio of the number of elements in each cluster to the degree of disorder of the Carnot cycle process corresponding to all elements in the cluster according to the order of collection is used as the effectiveness of the cluster.

[0070] As an implementation manner, specifically, for the nth cluster, the mathematical formula for the effectiveness of the cluster is:

[0071]

[0072] Where, d n Indicates the effectiveness of the nth cluster, C n Represents the number of Carnot cycles in the nth cluster. n It represents the variance of all Carnot cycle processes in the nth cluster in the order of collection. For example, if the Carnot cycle processes in this cluster are the 2nd, 4th, 5th, and 6th Carnot cycle processes, then the variance of the numbers 2, 4, 5, and 6 is taken as σ n .

[0073] It should be understood that C n The larger the value of , the more Carnot cycle processes are contained in the cluster; n The smaller the value of , the closer the distance between the Carnot cycle processes in the nth cluster is, so d n The larger the value of , the greater the effectiveness of the Carnot cycle process of the nth cluster.

[0074] As another embodiment, the standard deviation of all Carnot cycle processes in the nth cluster according to the order of collection can be used as σ n .

[0075] The effectiveness of each cluster can be calculated using the above method. The greater the effectiveness of a cluster, the more relevant the wind chamber parameters corresponding to the Carnot cycle process in that cluster are to subsequent Carnot cycles. Therefore, the effectiveness is normalized and recorded as a weight.

[0076] Taking the nth cluster as an example, the mathematical formula for its corresponding weight value is: Where λ n Indicates the weight value of the nth cluster, d n Indicates the effectiveness of the nth cluster, and N represents the number of clusters after clustering all historical Carnot cycle processes.

[0077] S6, using the weight value to perform weighted averaging on the average values ​​of various parameters in all Carnot cycle processes in the corresponding cluster, to obtain the optimal value of each parameter, and to control various parameters in subsequent Carnot cycle processes through the controller.

[0078] According to the above method, the weight value of each cluster can be calculated, and then the wind chamber parameters in all Carnot cycle processes in the corresponding cluster are weighted averaged to determine the optimal parameter values ​​of the wind chamber under various parameters in the subsequent Carnot cycle process.

[0079] As an implementation method, specifically, the mathematical formula for the optimal value of the kth parameter is:

[0080]

[0081] Where V k represents the optimal value of the kth parameter, N represents the number of clusters after clustering all historical Carnot cycle processes, and λ n Indicates the weight value of the nth cluster, μ n,k It represents the average value of the kth parameter in all Carnot cycle processes of the nth cluster.

[0082] The above method can be used to calculate the optimal value of each parameter. In the subsequent Carnot cycle process, the calculated optimal value is set as the ideal value for the corresponding parameter in the wind chamber. The deviation between the ideal value and the measured value is used as the input of the PID controller, which controls the corresponding parameter in the subsequent Carnot cycle process.

[0083] In other embodiments of the present application, a PI controller may also be used to control the corresponding parameters.

[0084] At this point, the above method can be used to complete the coordinated control of the solid heat storage system during operation.

[0085] The various embodiments in this application are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0086] It should be noted that, unless otherwise specified and limited, terms such as "include", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, the phrase "including a ..." defines an element, does not exclude the presence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items.

[0087] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not invented herein.

[0088] It will be understood that the present application is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

Claims

1. A coordinated control method for an energy storage power station system based on a Carnot cycle battery, characterized in that: The method comprises the following steps: S1, obtaining various parameters collected by each wind chamber at all collection times during several historical Carnot cycles of the energy storage power station coefficient; S2, based on the range and overall change trend of the change curve of each parameter of each air chamber during the Carnot cycle, determine the change characteristic value of each parameter of each air chamber during each Carnot cycle; S3, based on the average level of the difference in the characteristic values ​​of various parameters between Carnot cycle processes under the same wind chamber, all historical Carnot cycle processes are clustered; S4, based on the number of clusters in the clustering results, the maximum number of elements in the clusters and the difference between the number of all historical Carnot cycle processes, determine the stability and judge whether the historical Carnot cycle process can be used for parameter control in the subsequent Carnot cycle process; S5, determining the effectiveness of each cluster based on the number of elements in each cluster and the degree of disorder of the order of distribution of all elements in the Carnot cycle process according to the order of collection; normalizing the effectiveness and recording it as a weight value; S6, using the weight value to perform weighted averaging on the average values ​​of various parameters in all Carnot cycle processes in the corresponding cluster, to obtain the optimal value of each parameter, and to control various parameters in subsequent Carnot cycle processes through the controller.

2. The coordinated control method for an energy storage power station system based on a Carnot cycle battery according to claim 1, characterized in that: The types of the air chambers include high-temperature air chambers and low-temperature air chambers.

3. The coordinated control method for an energy storage power station system based on a Carnot cycle battery according to claim 1, characterized in that: The variation curve is obtained by curve fitting the data collected at all collection moments for each parameter of each air chamber during the Carnot cycle.

4. The coordinated control method for an energy storage power station system based on a Carnot cycle battery according to claim 1, characterized in that: The change characteristic value is determined by the product of the range and the overall change trend.

5. The coordinated control method for an energy storage power station system based on a Carnot cycle battery according to claim 3, characterized in that: The overall change trend is further determined by the average of the slopes of all points corresponding to the acquisition moments on the change curve.

6. The coordinated control method for an energy storage power station system based on a Carnot cycle battery according to claim 1, characterized in that: When clustering all historical Carnot cycle processes, the clustering distance between the Carnot cycle processes is further determined as: Under the same parameter of the same wind chamber, the difference between the change characteristic values ​​in any two historical Carnot cycle processes is calculated; the average level of the difference of all parameters under all wind chambers is used as the clustering distance between any two historical Carnot cycle processes.

7. The coordinated control method for an energy storage power station system based on a Carnot cycle battery according to claim 1, characterized in that: In step S4, the stability is further determined by the ratio of the difference to the number of clusters.

8. The coordinated control method for an energy storage power station system based on a Carnot cycle battery according to claim 7, characterized in that: When the stability is greater than or equal to a preset stability threshold, the historical Carnot cycle process can be used for parameter control in subsequent Carnot cycle processes.

9. The coordinated control method for an energy storage power station system based on a Carnot cycle battery according to claim 1, characterized in that: The effectiveness is determined by the ratio of the number of elements in the corresponding cluster to the degree of disorder of the order distribution.

10. The coordinated control method for an energy storage power station system based on a Carnot cycle battery according to claim 1, characterized in that: The method for controlling various parameters in the subsequent Carnot cycle process by the controller is: The optimal value is used as the ideal value of the corresponding parameter, and the deviation between the ideal value and the measured value is used as the input of the controller, and the controller controls the corresponding parameter in the subsequent Carnot cycle process.

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